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Weakly Supervised Training to Achieve 99% Accuracy for Retail Asset Protection

Bhavesh Patel , Dell EMC | Matt Scott, Malong Technologies

GTC 2020

We'll present an efficient way to train deep neural networks on large-scale, weakly-supervised data learned from regular retail customer asset-protection behaviors, without any expert annotation. We'll develop a principled learning strategy by leveraging curriculum learning to effectively handle a massive number of noisy labels and data imbalance. Our new learning curriculum measures the complexity of data using its distribution density in a feature space, and ranks that complexity without supervision. This allows for an efficient implementation of curriculum learning on large-scale retail images, resulting in a high-performance convolutional neural network model, where the negative impact of noisy labels is reduced substantially.

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